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mirea-projects/Third term/Artificial intelligence systems and big data/5.ipynb

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2024-09-23 23:22:33 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Рабочая тетрадь No 5"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import tree, metrics\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.3 Задание\n",
"\n",
"Создайте класс по работе с тригонометрическими функциями. В классе \n",
"должны быть реализованы функции вычисления: \n",
"- косинуса; \n",
"- синуса; \n",
"- тангенса; \n",
"- арксинуса; \n",
"- арккосинуса; \n",
"- арктангенса; \n",
"- перевода из градусов в радианы."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"class TrigFunctions:\n",
" def __init__(self, precision=10):\n",
" # Константа для pi\n",
" self.pi = 3.141592653589793\n",
" # Переменная точности для вычислений\n",
" self.precision = precision\n",
"\n",
" # Факториал для ряда Тейлора\n",
" def factorial(self, n):\n",
" result = 1\n",
" for i in range(2, n + 1):\n",
" result *= i\n",
" return result\n",
"\n",
" # Приближенное вычисление синуса с помощью ряда Тейлора\n",
" def sin(self, angle_radians):\n",
" sin_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n + 1)) / self.factorial(2 * n + 1)\n",
" sin_approx += sign * term\n",
" \n",
" return sin_approx\n",
"\n",
" # Приближенное вычисление косинуса с помощью ряда Тейлора\n",
" def cos(self, angle_radians):\n",
" cos_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n)) / self.factorial(2 * n)\n",
" cos_approx += sign * term\n",
" \n",
" return cos_approx\n",
"\n",
" # Приближенное вычисление тангенса как sin/cos\n",
" def tan(self, angle_radians):\n",
" return self.sin(angle_radians) / self.cos(angle_radians)\n",
"\n",
" # Приближенное вычисление арксинуса с использованием метода Ньютона\n",
" def arcsin(self, value):\n",
" if value < -1 or value > 1:\n",
" return None # Арксинус определен только на отрезке [-1, 1]\n",
" \n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.sin(x) - value) / self.cos(x)\n",
" \n",
" return x\n",
"\n",
" # Арккосинус как pi/2 - арксинус\n",
" def arccos(self, value):\n",
" return self.pi / 2 - self.arcsin(value)\n",
"\n",
" # Приближенное вычисление арктангенса с использованием метода Ньютона\n",
" def arctan(self, value):\n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.tan(x) - value) / (1 + value ** 2)\n",
" \n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Угол (рад): 0.7853981633974483\n",
"sin: 0.7071067811865475\n",
"cos: 0.7071067811865475\n",
"tan: 1.0\n",
"arcsin (рад): 0.7853981633974483\n",
"arccos (рад): 0.7853981633974483\n",
"arctan (рад): 0.7853981633974483\n"
]
}
],
"source": [
"trig_functions = TrigFunctions(precision=10)\n",
"\n",
"angle = 3.141592653589793 / 4 # pi/4 радиан (45 градусов)\n",
"\n",
"sin_value = trig_functions.sin(angle)\n",
"cos_value = trig_functions.cos(angle)\n",
"tan_value = trig_functions.tan(angle)\n",
"arcsin_value = trig_functions.arcsin(sin_value)\n",
"arccos_value = trig_functions.arccos(cos_value)\n",
"arctan_value = trig_functions.arctan(tan_value)\n",
"\n",
"print(\"Угол (рад):\", angle)\n",
"print(\"sin:\", sin_value)\n",
"print(\"cos:\", cos_value)\n",
"print(\"tan:\", tan_value)\n",
"print(\"arcsin (рад):\", arcsin_value)\n",
"print(\"arccos (рад):\", arccos_value)\n",
"print(\"arctan (рад):\", arctan_value)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 1\n",
"\n",
"Представьте дерево показанное на рисунке с использованием списка из \n",
"списков. Выведите на печать корень дерева, а также его левое и правое \n",
"поддеревья."
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Корень: a\n",
"Левое поддерево: ['b', ['d', []], ['e', []]]\n",
"Правое поддерево: ['c', ['f', []]]\n"
]
}
],
"source": [
"tr = ['a', ['b', ['d', []], ['e', []]], ['c', ['f', []]]]\n",
"\n",
"print(f\"Корень: {tr[0]}\")\n",
"print(f\"Левое поддерево: {tr[1]}\")\n",
"print(f\"Правое поддерево: {tr[2]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 2\n",
"\n",
"Дан класс, описывающий бинарное дерево. \n",
"\n",
"\n",
"```python\n",
"class Tree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data \n",
" def PrintTree(self): \n",
" print(self.data) \n",
"```\n",
"Реализуйте в классе функцию для вставки нового элемента в дерево по \n",
"следующим правилам: \n",
" \n",
"- Левое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Правое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Каждое из левого и правого поддеревьев также должно быть \n",
"бинарным деревом поиска. \n",
"- Не должно быть повторяющихся узлов. \n",
"\n",
"Метод вставки сравнивает значение узла с родительским узлом и решает \n",
"куда доваить элемент (в левое или правое поддерево). Перепишите, метод \n",
"PrintTree для печати полной версии дерева."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"class BinaryTree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data\n",
"\n",
" def insert(self, data):\n",
" if data < self.data:\n",
" if self.left is None:\n",
" self.left = BinaryTree(data)\n",
" else:\n",
" self.left.insert(data)\n",
" elif data > self.data:\n",
" if self.right is None:\n",
" self.right = BinaryTree(data)\n",
" else:\n",
" self.right.insert(data)\n",
"\n",
" def Print(self, level=0):\n",
" if self.right:\n",
" self.right.Print(level + 1)\n",
" \n",
" print(' ' * 4 * level + '->', self.data)\n",
" if self.left:\n",
" self.left.Print(level + 1) "
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" -> 17\n",
" -> 15\n",
" -> 12\n",
"-> 10\n",
" -> 7\n",
" -> 5\n",
" -> 3\n"
]
}
],
"source": [
"root = BinaryTree(10)\n",
"root.insert(5)\n",
"root.insert(15)\n",
"root.insert(3)\n",
"root.insert(7)\n",
"root.insert(12)\n",
"root.insert(17)\n",
"\n",
"root.Print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3.1 Задание\n",
"\n",
"Постройте классификатор на основе дерева принятия решений следующего датасета:\n",
"\n",
"```python\n",
"X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
"target = [0, 0, 0, 1, 1, 1]\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"ds = pd.DataFrame(np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]))\n",
"target = [0, 0, 0, 1, 1, 1]"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(ds, target, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 0.75, 'x[1] <= -0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
" Text(0.25, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
" Text(0.375, 0.5, 'True '),\n",
" Text(0.75, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
" Text(0.625, 0.5, ' False')]"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classifier = DecisionTreeClassifier()\n",
"classifier.fit(x_train, y_train)\n",
"\n",
"tree.plot_tree(classifier)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0]\n",
"[[2]]\n",
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 2\n",
"\n",
" accuracy 1.00 2\n",
" macro avg 1.00 1.00 1.00 2\n",
"weighted avg 1.00 1.00 1.00 2\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/nktkln/.pyenv/versions/3.11.0/lib/python3.11/site-packages/sklearn/metrics/_classification.py:409: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
" warnings.warn(\n"
]
}
],
"source": [
"y_pred = classifier.predict(x_test)\n",
"\n",
"print(y_pred)\n",
"print(confusion_matrix(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.1 Задание\n",
"\n",
"Задание. Постройте модель регрессии для данных из предыдущей рабочей \n",
"тетради.Для примера можно взять потребления газа (в миллионах \n",
"галлонов) в 48 штатах США или набор данных о качестве красного вина: \n",
"https://raw.githubusercontent.com/likarajo/petrol_consumption/master/data/petrol_consumption.csv \n",
"https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv \n",
"\n",
"Постройте прогноз. Оцените точность модели."
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.319637</td>\n",
" <td>0.527821</td>\n",
" <td>0.270976</td>\n",
" <td>2.538806</td>\n",
" <td>0.087467</td>\n",
" <td>15.874922</td>\n",
" <td>46.467792</td>\n",
" <td>0.996747</td>\n",
" <td>3.311113</td>\n",
" <td>0.658149</td>\n",
" <td>10.422983</td>\n",
" <td>5.636023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.741096</td>\n",
" <td>0.179060</td>\n",
" <td>0.194801</td>\n",
" <td>1.409928</td>\n",
" <td>0.047065</td>\n",
" <td>10.460157</td>\n",
" <td>32.895324</td>\n",
" <td>0.001887</td>\n",
" <td>0.154386</td>\n",
" <td>0.169507</td>\n",
" <td>1.065668</td>\n",
" <td>0.807569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.600000</td>\n",
" <td>0.120000</td>\n",
" <td>0.000000</td>\n",
" <td>0.900000</td>\n",
" <td>0.012000</td>\n",
" <td>1.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.990070</td>\n",
" <td>2.740000</td>\n",
" <td>0.330000</td>\n",
" <td>8.400000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7.100000</td>\n",
" <td>0.390000</td>\n",
" <td>0.090000</td>\n",
" <td>1.900000</td>\n",
" <td>0.070000</td>\n",
" <td>7.000000</td>\n",
" <td>22.000000</td>\n",
" <td>0.995600</td>\n",
" <td>3.210000</td>\n",
" <td>0.550000</td>\n",
" <td>9.500000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.900000</td>\n",
" <td>0.520000</td>\n",
" <td>0.260000</td>\n",
" <td>2.200000</td>\n",
" <td>0.079000</td>\n",
" <td>14.000000</td>\n",
" <td>38.000000</td>\n",
" <td>0.996750</td>\n",
" <td>3.310000</td>\n",
" <td>0.620000</td>\n",
" <td>10.200000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>9.200000</td>\n",
" <td>0.640000</td>\n",
" <td>0.420000</td>\n",
" <td>2.600000</td>\n",
" <td>0.090000</td>\n",
" <td>21.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.997835</td>\n",
" <td>3.400000</td>\n",
" <td>0.730000</td>\n",
" <td>11.100000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>15.900000</td>\n",
" <td>1.580000</td>\n",
" <td>1.000000</td>\n",
" <td>15.500000</td>\n",
" <td>0.611000</td>\n",
" <td>72.000000</td>\n",
" <td>289.000000</td>\n",
" <td>1.003690</td>\n",
" <td>4.010000</td>\n",
" <td>2.000000</td>\n",
" <td>14.900000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \\\n",
"mean 8.319637 0.527821 0.270976 2.538806 \n",
"std 1.741096 0.179060 0.194801 1.409928 \n",
"min 4.600000 0.120000 0.000000 0.900000 \n",
"25% 7.100000 0.390000 0.090000 1.900000 \n",
"50% 7.900000 0.520000 0.260000 2.200000 \n",
"75% 9.200000 0.640000 0.420000 2.600000 \n",
"max 15.900000 1.580000 1.000000 15.500000 \n",
"\n",
" chlorides free sulfur dioxide total sulfur dioxide density \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \\\n",
"mean 0.087467 15.874922 46.467792 0.996747 \n",
"std 0.047065 10.460157 32.895324 0.001887 \n",
"min 0.012000 1.000000 6.000000 0.990070 \n",
"25% 0.070000 7.000000 22.000000 0.995600 \n",
"50% 0.079000 14.000000 38.000000 0.996750 \n",
"75% 0.090000 21.000000 62.000000 0.997835 \n",
"max 0.611000 72.000000 289.000000 1.003690 \n",
"\n",
" pH sulphates alcohol quality \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 3.311113 0.658149 10.422983 5.636023 \n",
"std 0.154386 0.169507 1.065668 0.807569 \n",
"min 2.740000 0.330000 8.400000 3.000000 \n",
"25% 3.210000 0.550000 9.500000 5.000000 \n",
"50% 3.310000 0.620000 10.200000 6.000000 \n",
"75% 3.400000 0.730000 11.100000 6.000000 \n",
"max 4.010000 2.000000 14.900000 8.000000 "
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv'\n",
"\n",
"ds = pd.read_csv(url)\n",
"\n",
"ds.describe()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"X = ds.iloc[:, :-1].values\n",
"y = ds.iloc[:, -1].values\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
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{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"regressor = DecisionTreeRegressor()\n",
"regressor.fit(X_train, y_train)\n",
"tree.plot_tree(regressor)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Actual Predicted\n",
"0 6 5.0\n",
"1 5 6.0\n",
"2 7 7.0\n",
"3 6 5.0\n",
"4 5 5.0\n",
".. ... ...\n",
"315 6 6.0\n",
"316 4 7.0\n",
"317 5 5.0\n",
"318 4 5.0\n",
"319 6 7.0\n",
"\n",
"[320 rows x 2 columns]\n"
]
}
],
"source": [
"y_pred = regressor.predict(X_test)\n",
"df = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 0.709375\n",
"MAE: 0.465625\n"
]
},
{
"data": {
"text/plain": [
"8.261588715046605"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
"print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
"\n",
"metrics.mean_absolute_error(y_test, y_pred) / np.average(y) * 100"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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